A series of stochastic models for human behavior analysis
Thi Thi Zin, Pyke Tin, Takashi Toriu, Hiromitsu Hama · 2012
In this paper, we propose a new approach to analyze human behaviors by using a series of stochastic models composed of a Bivariate Gamma Markov model, a two dimensional correlated Random Walk model and a finite state Markov Chain model. Specifically, the proposed method contains three modules namely: (i) image analysis module, (ii) probability analysis module and (iii) event analysis module. We model each module by a special type of stochastic processes forming a series of stochastic models for the complete behavior analysis system. This approach is more effective in utilizing modular stochastic models to describe complex behavior patterns. By assembling these modular models in a series we can design a robust model for the analysis of human behaviors. The feasibility and effectiveness of the proposed method are tested on two different datasets: a self-collected dataset and PETS 2006 dataset.